Troubleshooting Neural Network Backpropagation Code Not Working

Discover the solutions to making your `neural network` backpropagation algorithm work effectively by exploring common pitfalls and fixes.
---
This video is based on the question https://stackoverflow.com/q/66394626/ asked by the user 'wjmccann' ( https://stackoverflow.com/u/7059087/ ) and on the answer https://stackoverflow.com/a/66418278/ provided by the user 'wjmccann' ( https://stackoverflow.com/u/7059087/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Neural Network Backpropogation code not working

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/licensing
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Troubleshooting Neural Network Backpropagation Code Not Working

Creating a neural network can be a challenging endeavor, especially when dealing with complex concepts like backpropagation. A common problem encountered is when the backpropagation code does not yield the expected results or fails to converge. In this guide, we'll discuss a specific scenario involving the XOR dataset and how to fix issues in the backpropagation algorithm for a simple neural network model.

The Problem: Non-Convergence in Backpropagation

The objective of the author was to create a simple neural network with

1 Output Node

1 Hidden Layer with 3 Nodes

Input Layer of Variable Size (for XOR functionality, 3 input nodes were used where one node represents bias)

However, the author's backpropagation code did not converge correctly on the XOR data. Despite the equations and transformations seeming correct, a consistent pattern of bouncing error values was observed. This is a frustrating issue that can halt progress in neural network development.

Understanding the Solution

Upon reviewing the provided code snippets and the approach taken, we can pinpoint the root cause of the problem. This section delves into the solution step by step.

1. The Feed-Forward Function

The implementation starts with a feed_forward_predict function, which operates as follows:

[[See Video to Reveal this Text or Code Snippet]]

The purpose of this function is to calculate the output of the neural network given the input data and the current weights. It's vital that this function works correctly before backpropagation is implemented.

2. Calculating the Objective Function

The next step involves calculating the objective function to measure how well the network predicts the labels.

[[See Video to Reveal this Text or Code Snippet]]

This function accurately computes the Mean Squared Error (MSE) between the network’s predictions and the actual labels.

3. Revising the Backpropagation Algorithm

The main issue identified was in the backpropagation phase where gradients for the weights connecting hidden to output layers were computed. The original implementation incorrectly reassigned values for weight derivatives instead of summing them across iterations.

Here’s the correction to the backpropagation loop:

[[See Video to Reveal this Text or Code Snippet]]

4. Conclusion and Key Takeaway

By ensuring that the weight gradients were summed rather than overwritten in each iteration, the network began to converge correctly on the XOR dataset. The mathematical operations and structures were already in place; thus, a simple fix led to the desired behavior of the neural network.

Final Notes

Debugging: Always print out error values during training to understand the behavior of your network.

Consistency: Use a fixed random seed for reproducibility when initializing weights.

Patience: Tuning neural network hyperparameters such as the learning rate and epoch count can have varying effects on convergence.

In summary, addressing small coding errors can have substantial impacts on neural network functionality. Always double-check your gradient calculations in the backpropagation step, as these are crucial for effective learning.

Видео Troubleshooting Neural Network Backpropagation Code Not Working канала vlogize